Upload README.md

README.md

@@ -0,0 +1,242 @@
+---
+base_model: Omartificial-Intelligence-Space/Arabic-Triplet-Matryoshka-V2
+datasets:
+- Omartificial-Intelligence-Space/Arabic-stsb
+- Omartificial-Intelligence-Space/Arabic-NLi-Pair-Class
+language:
+- ar
+library_name: sentence-transformers
+metrics:
+- pearson_cosine
+- spearman_cosine
+- pearson_manhattan
+- spearman_manhattan
+- pearson_euclidean
+- spearman_euclidean
+- pearson_dot
+- spearman_dot
+- pearson_max
+- spearman_max
+pipeline_tag: sentence-similarity
+tags:
+- mteb
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:947818
+- loss:SoftmaxLoss
+- loss:CosineSimilarityLoss
+- transformers
+model-index:
+- name: Omartificial-Intelligence-Space/GATE-AraBert-v1
+  results:
+  - dataset:
+      config: ar-ar
+      name: MTEB STS17 (ar-ar)
+      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
+      split: test
+      type: mteb/sts17-crosslingual-sts
+    metrics:
+    - type: cosine_pearson
+      value: 82.06597171670848
+    - type: cosine_spearman
+      value: 82.7809395809498
+    - type: euclidean_pearson
+      value: 79.23996991139896
+    - type: euclidean_spearman
+      value: 81.5287595404711
+    - type: main_score
+      value: 82.7809395809498
+    - type: manhattan_pearson
+      value: 78.95407006608013
+    - type: manhattan_spearman
+      value: 81.15109493737467
+    task:
+      type: STS
+  - dataset:
+      config: ar
+      name: MTEB STS22.v2 (ar)
+      revision: d31f33a128469b20e357535c39b82fb3c3f6f2bd
+      split: test
+      type: mteb/sts22-crosslingual-sts
+    metrics:
+    - type: cosine_pearson
+      value: 54.912880452465004
+    - type: cosine_spearman
+      value: 63.09788380910325
+    - type: euclidean_pearson
+      value: 57.92665617677832
+    - type: euclidean_spearman
+      value: 62.76032598469037
+    - type: main_score
+      value: 63.09788380910325
+    - type: manhattan_pearson
+      value: 58.0736648155273
+    - type: manhattan_spearman
+      value: 62.94190582776664
+    task:
+      type: STS
+  - dataset:
+      config: ar
+      name: MTEB STS22 (ar)
+      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
+      split: test
+      type: mteb/sts22-crosslingual-sts
+    metrics:
+    - type: cosine_pearson
+      value: 51.72534929358701
+    - type: cosine_spearman
+      value: 59.75149627160101
+    - type: euclidean_pearson
+      value: 53.894835373598774
+    - type: euclidean_spearman
+      value: 59.44278354697161
+    - type: main_score
+      value: 59.75149627160101
+    - type: manhattan_pearson
+      value: 54.076675975406985
+    - type: manhattan_spearman
+      value: 59.610061143235725
+    task:
+      type: STS
+widget:
+- source_sentence: امرأة تكتب شيئاً
+  sentences:
+  - مراهق يتحدث إلى فتاة عبر كاميرا الإنترنت
+  - امرأة تقطع البصل الأخضر.
+  - مجموعة من كبار السن يتظاهرون حول طاولة الطعام.
+- source_sentence: تتشكل النجوم في مناطق تكوين النجوم، والتي تنشأ نفسها من السحب الجزيئية.
+  sentences:
+  - لاعب كرة السلة على وشك تسجيل نقاط لفريقه.
+  - المقال التالي مأخوذ من نسختي من "أطلس البطريق الجديد للتاريخ الوسطى"
+  - قد يكون من الممكن أن يوجد نظام شمسي مثل نظامنا خارج المجرة
+- source_sentence: >-
+    تحت السماء الزرقاء مع الغيوم البيضاء، يصل طفل لمس مروحة طائرة واقفة على حقل
+    من العشب.
+  sentences:
+  - امرأة تحمل كأساً
+  - طفل يحاول لمس مروحة طائرة
+  - اثنان من عازبين عن الشرب يستعدون للعشاء
+- source_sentence: رجل في منتصف العمر يحلق لحيته في غرفة ذات جدران بيضاء والتي لا تبدو كحمام
+  sentences:
+  - فتى يخطط اسمه على مكتبه
+  - رجل ينام
+  - المرأة وحدها وهي نائمة في غرفة نومها
+- source_sentence: الكلب البني مستلقي على جانبه على سجادة بيج، مع جسم أخضر في المقدمة.
+  sentences:
+  - شخص طويل القامة
+  - المرأة تنظر من النافذة.
+  - لقد مات الكلب
+license: apache-2.0
+---
+
+# GATE-AraBert-V1
+
+This is **GATE | General Arabic Text Embedding** trained using SentenceTransformers in a **multi-task** setup. The system trains on the **AllNLI** and on the **STS** dataset.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [Omartificial-Intelligence-Space/Arabic-Triplet-Matryoshka-V2](https://huggingface.co/Omartificial-Intelligence-Space/Arabic-Triplet-Matryoshka-V2) <!-- at revision 5ce4f80f3ede26de623d6ac10681399dba5c684a -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 tokens
+- **Similarity Function:** Cosine Similarity
+- **Training Datasets:**
+    - [all-nli](https://huggingface.co/datasets/Omartificial-Intelligence-Space/Arabic-NLi-Pair-Class)
+    - [sts](https://huggingface.co/datasets/Omartificial-Intelligence-Space/arabic-stsb)
+- **Language:** ar
+
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("Omartificial-Intelligence-Space/GATE-AraBert-v1")
+# Run inference
+sentences = [
+    'الكلب البني مستلقي على جانبه على سجادة بيج، مع جسم أخضر في المقدمة.',
+    'لقد مات الكلب',
+    'شخص طويل القامة',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+
+## Evaluation
+
+### Metrics
+
+#### Semantic Similarity
+* Dataset: `sts-dev`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value     |
+|:--------------------|:----------|
+| pearson_cosine      | 0.8391    |
+| **spearman_cosine** | **0.841** |
+| pearson_manhattan   | 0.8277    |
+| spearman_manhattan  | 0.8361    |
+| pearson_euclidean   | 0.8274    |
+| spearman_euclidean  | 0.8358    |
+| pearson_dot         | 0.8154    |
+| spearman_dot        | 0.818     |
+| pearson_max         | 0.8391    |
+| spearman_max        | 0.841     |
+
+#### Semantic Similarity
+* Dataset: `sts-test`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.813      |
+| **spearman_cosine** | **0.8173** |
+| pearson_manhattan   | 0.8114     |
+| spearman_manhattan  | 0.8164     |
+| pearson_euclidean   | 0.8103     |
+| spearman_euclidean  | 0.8158     |
+| pearson_dot         | 0.7908     |
+| spearman_dot        | 0.7887     |
+| pearson_max         | 0.813      |
+| spearman_max        | 0.8173     |
+
+
+## <span style="color:blue">Acknowledgments</span>
+
+The author would like to thank Prince Sultan University for their invaluable support in this project. Their contributions and resources have been instrumental in the development and fine-tuning of these models.
+
+
+```markdown
+## Citation
+
+If you use the GATE, please cite it as follows:
+
+@misc{nacar2025GATE,
+      title={GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Hybrid Loss Training}, 
+      author={Omer Nacar, Anis Koubaa, Serry Taiseer Sibaee and Lahouari Ghouti},
+      year={2025},
+      note={Submitted to COLING 2025},
+      url={https://huggingface.co/Omartificial-Intelligence-Space/GATE-AraBert-v1},
+}
+
+